We approach the mitosis candidate detection problem as a segmentation task. However, in order to train a CNN for the segmentation task in a supervised manner, GT masks of the desired objects within the image are required. Since the organizers have only provided approximate bounding box annotations for each mitosis in the released MIDOG dataset, we obtained mitotic instance segmentation masks using NuClickhttps://github.com/navidstuv/NuClick [1]}, [2]}, a CNN-based interactive segmentation model capable of generating precise segmentation masks for each mitotic figure from a point annotation within the mitotic figure. Therefore, for each annotation point in the dataset, we fed the centre point of the bounding box alongside the patch from the original image into NuClick to generate the individual segmentation mask.
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